Sand Dune Velocity Determination via Cross-Domain Regularization
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Solution Overview
Problem
In oil exploration, high dunes in arid regions like the Arabian Peninsula cause false structures in seismic images due to uncorrected dune effects, leading to potential erroneous drilling for hydrocarbons, as conventional seismic approaches struggle with accurate velocity determinations and poor spatial sampling.
Innovation Solution
The use of airborne electromagnetic (AEM) data and seismic data, combined through cross-domain regularization, to generate a velocity-depth model that identifies velocity variations within sand dunes, thereby correcting seismic images and reducing the risk of false structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional seismic approaches are used for velocity determination in sand dune areas, then seismic data can be acquired, but the velocity determinations are inaccurate and false structures appear in seismic images
Solution Approach 1:
The patent introduces airborne electromagnetic (AEM) data as an intermediary to bridge the gap between seismic data and true subsurface velocity. The AEM data provides independent constraints on subsurface resistivity structure, which is then integrated with seismic data through cross-domain regularization to produce accurate velocity models that eliminate false dune structures in seismic images.
Solution Approach 2:
The patent creates a composite imaging approach by combining two different geophysical domains (seismic and electromagnetic) into a unified velocity model. This composite method leverages the complementary strengths of both methods: seismic provides structural information while AEM provides resistivity constraints, together producing reliable velocity determinations that remove false structures.
2Measurement precision
If airborne electromagnetic (AEM) data and seismic data are combined through cross-domain regularization, then velocity-depth models with accurate velocity variations are generated, but the processing complexity increases
Solution Approach 1:
The patent develops a multi-functional processing framework that simultaneously performs multiple tasks: AEM data inversion, seismic data processing, cross-domain regularization, and velocity model generation. This universal approach integrates what would otherwise be separate processing workflows into a unified system that efficiently handles the complexity of combining multiple geophysical domains.
Solution Approach 2:
The patent transforms the complex cross-domain regularization problem into a more manageable form by changing parameters and variables in the optimization process. The inversion process uses parameter transformations to reconcile differences between AEM and seismic domains, enabling accurate velocity determination while managing computational complexity through mathematical parameter optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively eliminates or reduces false structures in seismic images, enhancing the accuracy of seismic imaging and reducing the risk of erroneous drilling by providing robust velocity determinations within sand dunes, thus improving the success rate of hydrocarbon exploration.
Implementation Method 1
airborne electromagnetic (AEM) data and seismic data for a geographic region, including sand dunes, are received, and the AEM data identifies apparent resistivity as a function of depth within the sand dunes
Data Source
AI summary
In some implementations, airborne electromagnetic (AEM) data and seismic data for a geographic region including sand dunes are received, and the AEM data identifies apparent resistivity as a function of depth within the sand dunes. An inversion with cross-domain regularization is calculated of the AEM data and the seismic data to generate a velocity-depth model, and the velocity depth model identifies velocity variations within the sand dunes. A seismic image using the velocity-depth model is generated.